| """Attention layers.""" | |
| import math | |
| import warnings | |
| from typing import Optional | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from packaging import version | |
| from torch import nn | |
| from .norm import LPLayerNorm | |
| def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool): | |
| if original_is_causal and num_query_tokens != num_key_tokens: | |
| if num_query_tokens != 1: | |
| raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.') | |
| else: | |
| return False | |
| return original_is_causal | |
| def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): | |
| q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads) | |
| kv_n_heads = 1 if multiquery else n_heads | |
| k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads) | |
| v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads) | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| k = torch.cat([past_key_value[0], k], dim=3) | |
| v = torch.cat([past_key_value[1], v], dim=2) | |
| past_key_value = (k, v) | |
| (b, _, s_q, d) = q.shape | |
| s_k = k.size(-1) | |
| if softmax_scale is None: | |
| softmax_scale = 1 / math.sqrt(d) | |
| attn_weight = q.matmul(k) * softmax_scale | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - s_q) | |
| _s_k = max(0, attn_bias.size(3) - s_k) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q): | |
| raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.') | |
| attn_weight = attn_weight + attn_bias | |
| min_val = torch.finfo(q.dtype).min | |
| if key_padding_mask is not None: | |
| if attn_bias is not None: | |
| warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') | |
| attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val) | |
| if is_causal and (not q.size(2) == 1): | |
| s = max(s_q, s_k) | |
| causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16) | |
| causal_mask = causal_mask.tril() | |
| causal_mask = causal_mask.to(torch.bool) | |
| causal_mask = ~causal_mask | |
| causal_mask = causal_mask[-s_q:, -s_k:] | |
| attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val) | |
| attn_weight = torch.softmax(attn_weight, dim=-1) | |
| if dropout_p: | |
| attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True) | |
| out = attn_weight.to(v.dtype).matmul(v) | |
| out = rearrange(out, 'b h s d -> b s (h d)') | |
| if needs_weights: | |
| return (out, attn_weight, past_key_value) | |
| return (out, None, past_key_value) | |
| def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]): | |
| for tensor in tensors: | |
| if tensor.dtype not in valid_dtypes: | |
| raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.') | |
| if not tensor.is_cuda: | |
| raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).') | |
| def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): | |
| try: | |
| from flash_attn import bert_padding, flash_attn_interface | |
| except: | |
| raise RuntimeError('Please install flash-attn==1.0.3.post0') | |
| check_valid_inputs(query, key, value) | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| key = torch.cat([past_key_value[0], key], dim=1) | |
| value = torch.cat([past_key_value[1], value], dim=1) | |
| past_key_value = (key, value) | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - query.size(1)) | |
| _s_k = max(0, attn_bias.size(3) - key.size(1)) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if attn_bias is not None: | |
| raise NotImplementedError(f'attn_bias not implemented for flash attn.') | |
| (batch_size, seqlen) = query.shape[:2] | |
| if key_padding_mask is None: | |
| key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool) | |
| query_padding_mask = key_padding_mask[:, -query.size(1):] | |
| (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask) | |
| query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads) | |
| (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask) | |
| key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) | |
| (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask) | |
| value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads) | |
| if multiquery: | |
| key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1)) | |
| value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1)) | |
| dropout_p = dropout_p if training else 0.0 | |
| reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) | |
| output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights) | |
| output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen) | |
| return (output, None, past_key_value) | |
| def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False): | |
| try: | |
| from .flash_attn_triton import flash_attn_func | |
| except: | |
| _installed = False | |
| if version.parse(torch.__version__) < version.parse('2.0.0'): | |
| _installed = True | |
| try: | |
| from flash_attn.flash_attn_triton import flash_attn_func | |
| except: | |
| _installed = False | |
| if not _installed: | |
| raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.') | |
| check_valid_inputs(query, key, value) | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| key = torch.cat([past_key_value[0], key], dim=1) | |
| value = torch.cat([past_key_value[1], value], dim=1) | |
| past_key_value = (key, value) | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - query.size(1)) | |
| _s_k = max(0, attn_bias.size(3) - key.size(1)) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if dropout_p: | |
| raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.') | |
| if needs_weights: | |
| raise NotImplementedError(f'attn_impl: triton cannot return attn weights.') | |
| if key_padding_mask is not None: | |
| warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') | |
| (b_size, s_k) = key_padding_mask.shape[:2] | |
| if attn_bias is None: | |
| attn_bias = query.new_zeros(b_size, 1, 1, s_k) | |
| attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min) | |
| query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads) | |
| key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) | |
| value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads) | |
| if multiquery: | |
| key = key.expand(*key.shape[:2], n_heads, key.size(-1)) | |
| value = value.expand(*value.shape[:2], n_heads, value.size(-1)) | |
| reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) | |
| attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale) | |
| output = attn_output.view(*attn_output.shape[:2], -1) | |
| return (output, None, past_key_value) | |
| class MultiheadAttention(nn.Module): | |
| """Multi-head self attention. | |
| Using torch or triton attention implementation enables user to also use | |
| additive bias. | |
| """ | |
| def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): | |
| super().__init__() | |
| self.attn_impl = attn_impl | |
| self.clip_qkv = clip_qkv | |
| self.qk_ln = qk_ln | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.softmax_scale = softmax_scale | |
| if self.softmax_scale is None: | |
| self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads) | |
| self.attn_dropout_p = attn_pdrop | |
| self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device) | |
| fuse_splits = (d_model, 2 * d_model) | |
| self.Wqkv._fused = (0, fuse_splits) | |
| if self.qk_ln: | |
| layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm | |
| self.q_ln = layernorm_class(self.d_model, device=device) | |
| self.k_ln = layernorm_class(self.d_model, device=device) | |
| if self.attn_impl == 'flash': | |
| self.attn_fn = flash_attn_fn | |
| elif self.attn_impl == 'triton': | |
| self.attn_fn = triton_flash_attn_fn | |
| if verbose: | |
| warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') | |
| elif self.attn_impl == 'torch': | |
| self.attn_fn = scaled_multihead_dot_product_attention | |
| if torch.cuda.is_available() and verbose: | |
| warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) | |
| self.out_proj._is_residual = True | |
| def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): | |
| qkv = self.Wqkv(x) | |
| if self.clip_qkv: | |
| qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) | |
| (query, key, value) = qkv.chunk(3, dim=2) | |
| key_padding_mask = attention_mask | |
| if self.qk_ln: | |
| dtype = query.dtype | |
| query = self.q_ln(query).to(dtype) | |
| key = self.k_ln(key).to(dtype) | |
| (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights) | |
| return (self.out_proj(context), attn_weights, past_key_value) | |
| class MultiQueryAttention(nn.Module): | |
| """Multi-Query self attention. | |
| Using torch or triton attention implementation enables user to also use | |
| additive bias. | |
| """ | |
| def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None): | |
| super().__init__() | |
| self.attn_impl = attn_impl | |
| self.clip_qkv = clip_qkv | |
| self.qk_ln = qk_ln | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.head_dim = d_model // n_heads | |
| self.softmax_scale = softmax_scale | |
| if self.softmax_scale is None: | |
| self.softmax_scale = 1 / math.sqrt(self.head_dim) | |
| self.attn_dropout_p = attn_pdrop | |
| self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device) | |
| fuse_splits = (d_model, d_model + self.head_dim) | |
| self.Wqkv._fused = (0, fuse_splits) | |
| if self.qk_ln: | |
| layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm | |
| self.q_ln = layernorm_class(d_model, device=device) | |
| self.k_ln = layernorm_class(self.head_dim, device=device) | |
| if self.attn_impl == 'flash': | |
| self.attn_fn = flash_attn_fn | |
| elif self.attn_impl == 'triton': | |
| self.attn_fn = triton_flash_attn_fn | |
| if verbose: | |
| warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.') | |
| elif self.attn_impl == 'torch': | |
| self.attn_fn = scaled_multihead_dot_product_attention | |
| if torch.cuda.is_available() and verbose: | |
| warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.') | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) | |
| self.out_proj._is_residual = True | |
| def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False): | |
| qkv = self.Wqkv(x) | |
| if self.clip_qkv: | |
| qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) | |
| (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2) | |
| key_padding_mask = attention_mask | |
| if self.qk_ln: | |
| dtype = query.dtype | |
| query = self.q_ln(query).to(dtype) | |
| key = self.k_ln(key).to(dtype) | |
| (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True) | |
| return (self.out_proj(context), attn_weights, past_key_value) | |
| def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id): | |
| if attn_impl == 'flash': | |
| return None | |
| elif attn_impl in ['torch', 'triton']: | |
| if alibi: | |
| if (prefix_lm or not causal) or use_sequence_id: | |
| return (1, n_heads, seq_len, seq_len) | |
| return (1, n_heads, 1, seq_len) | |
| elif prefix_lm or use_sequence_id: | |
| return (1, 1, seq_len, seq_len) | |
| return None | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8): | |
| if attn_impl == 'flash': | |
| return None | |
| elif attn_impl in ['torch', 'triton']: | |
| if alibi: | |
| (device, dtype) = (attn_bias.device, attn_bias.dtype) | |
| attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype)) | |
| return attn_bias | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| def gen_slopes(n_heads, alibi_bias_max=8, device=None): | |
| _n_heads = 2 ** math.ceil(math.log2(n_heads)) | |
| m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device) | |
| m = m.mul(alibi_bias_max / _n_heads) | |
| slopes = 1.0 / torch.pow(2, m) | |
| if _n_heads != n_heads: | |
| slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads] | |
| return slopes.view(1, n_heads, 1, 1) | |
| def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None): | |
| alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len) | |
| if full: | |
| alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1) | |
| alibi_bias = alibi_bias.abs().mul(-1) | |
| slopes = gen_slopes(n_heads, alibi_bias_max, device=device) | |
| alibi_bias = alibi_bias * slopes | |
| return alibi_bias.to(dtype=dtype) | |
| ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention} | |